code: def test_mfccs():
      opts = get_config()
  
      transform = MFCCs(opts)
      num_frames = transform.num_frames
      assert num_frames == 8
      num_mfccs = transform.num_mfccs
      assert num_mfccs == 20
      N, C, fps = 10, 2, 41000
      audio_length = 1  # seconds
  
      audio = torch.rand(N, audio_length * fps, C)
      data = {
          "samples": {
              "audio": audio,
              "metadata": {
                  "audio_fps": fps,
              },
          }
      }
      result = transform(data)
      expected_frame_length = (
          11  # depends on num_frames, fps, audio_length
      )
      assert result["samples"]["audio"].shape == (
          N,
          C,
          num_mfccs,
          num_frames,
          expected_frame_length,
      )
  
file_path: ml-cvnets/tests/transforms/test_audio.py
imports: import copy
  import os
  import shutil
  import tempfile
  import pytest
  import scipy.io.wavfile
  import torch
  from data.transforms.audio import AudioResample, Gain, LambdaAudio, MFCCs, Noise, Roll, SetFixedLength, StandardizeChannels
  from tests.configs import get_config
label_desc: Use image node_id 1 for calling a global function with example usage: test_mfccs() without return types